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DSC-backstepping based robust adaptive NN control for strict-feedback nonlinear systems via small gain theorem

机译:基于DSC反推的鲁棒自适应NN控制,通过小增益定理用于严格反馈非线性系统

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摘要

The adaptive tracking control problem is discussed for a class of strict-feedback uncertain systems. RBF Neural Networks are used to approximate the uncertainties. A unified and systematic procedure is developed to derive a robust adaptive tracking controller with the fusion of dynamic surface control technique and small gain approach. The proposed algorithm can avoid both problems of 'explosion of complexity' and 'curse of dimension' synchronously, thus is convenient to implement in applications. The stability of the closed-loop system is proved. Finally, simulation results via two application examples validate the effectiveness and performance of the proposed scheme.
机译:针对一类严格反馈不确定系统,讨论了自适应跟踪控制问题。 RBF神经网络用于近似不确定性。通过将动态表面控制技术与小增益方法相结合,开发出一种统一,系统的程序来获得鲁棒的自适应跟踪控制器。所提出的算法可以同时避免“复杂性爆炸”和“维数诅咒”这两个问题,因此在应用中方便实现。证明了闭环系统的稳定性。最后,通过两个应用实例的仿真结果验证了所提方案的有效性和性能。

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